"""simple docstring"""


import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import DecisionTransformerModel
	from transformers.models.decision_transformer.modeling_decision_transformer import (
	    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
	)






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase=13      ,					_lowerCamelCase=7      ,					_lowerCamelCase=6      ,					_lowerCamelCase=17      ,					_lowerCamelCase=23      ,					_lowerCamelCase=11      ,					_lowerCamelCase=True      ,					):
										lowerCamelCase__   =parent
										lowerCamelCase__   =batch_size
										lowerCamelCase__   =seq_length
										lowerCamelCase__   =act_dim
										lowerCamelCase__   =state_dim
										lowerCamelCase__   =hidden_size
										lowerCamelCase__   =max_length
										lowerCamelCase__   =is_training

				def 						_a      ( self ):
										lowerCamelCase__   =floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
										lowerCamelCase__   =floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
										lowerCamelCase__   =floats_tensor((self.batch_size, self.seq_length, 1) )
										lowerCamelCase__   =floats_tensor((self.batch_size, self.seq_length, 1) )
										lowerCamelCase__   =ids_tensor((self.batch_size, self.seq_length)      ,					vocab_size=1000 )
										lowerCamelCase__   =random_attention_mask((self.batch_size, self.seq_length) )

										lowerCamelCase__   =self.get_config()

										return (
										    config,
										    states,
										    actions,
										    rewards,
										    returns_to_go,
										    timesteps,
										    attention_mask,
										)

				def 						_a      ( self ):
										return DecisionTransformerConfig(
										    batch_size=self.batch_size      ,					seq_length=self.seq_length      ,					act_dim=self.act_dim      ,					state_dim=self.state_dim      ,					hidden_size=self.hidden_size      ,					max_length=self.max_length      ,					)

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					):
										lowerCamelCase__   =DecisionTransformerModel(config=_lowerCamelCase )
										model.to(_lowerCamelCase )
										model.eval()
										lowerCamelCase__   =model(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )

										self.parent.assertEqual(result.state_preds.shape      ,					states.shape )
										self.parent.assertEqual(result.action_preds.shape      ,					actions.shape )
										self.parent.assertEqual(result.return_preds.shape      ,					returns_to_go.shape )
										self.parent.assertEqual(
										    result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length * 3, self.hidden_size) )  # seq length *3 as there are 3 modelities: states, returns and actions






				def 						_a      ( self ):
										lowerCamelCase__   =self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase__
										)   , (
										    lowerCamelCase__
										)   , (
										    lowerCamelCase__
										)   , (
										    lowerCamelCase__
										)   , (
										    lowerCamelCase__
										)   , (
										    lowerCamelCase__
										)   , (
										    lowerCamelCase__
										)   , 
										)   =config_and_inputs
										lowerCamelCase__   ={
										    "states": states,
										    "actions": actions,
										    "rewards": rewards,
										    "returns_to_go": returns_to_go,
										    "timesteps": timesteps,
										    "attention_mask": attention_mask,
										}
										return config, inputs_dict






@require_torch
class   __UpperCAmelCase		(      __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase      ):
				A__ :  List[str]    				=			(DecisionTransformerModel,) if is_torch_available() else ()
				A__ :  List[str]    				=			()
				A__ :  Any    				=			{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

				# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
				A__ :  Dict    				=			False

				# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
				A__ :  Dict    				=			False
				A__ :  List[Any]    				=			False
				A__ :  Union[str, Any]    				=			False
				A__ :  List[str]    				=			False
				A__ :  str    				=			False
				A__ :  Dict    				=			False
				A__ :  str    				=			False
				A__ :  Dict    				=			False
				A__ :  List[str]    				=			False

				def 						_a      ( self ):
										lowerCamelCase__   =DecisionTransformerModelTester(self )
										lowerCamelCase__   =ConfigTester(self      ,					config_class=_lowerCamelCase      ,					hidden_size=37 )

				def 						_a      ( self ):
										self.config_tester.run_common_tests()

				def 						_a      ( self ):
										lowerCamelCase__   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase )

				@slow
				def 						_a      ( self ):
										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowerCamelCase__   =DecisionTransformerModel.from_pretrained(_lowerCamelCase )
																self.assertIsNotNone(_lowerCamelCase )






				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowerCamelCase__   =model_class(_lowerCamelCase )
																lowerCamelCase__   =inspect.signature(model.forward )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																lowerCamelCase__   =[*signature.parameters.keys()]

																lowerCamelCase__   =[
																    "states",
																    "actions",
																    "rewards",
																    "returns_to_go",
																    "timesteps",
																    "attention_mask",
																]

																self.assertListEqual(arg_names[: len(_lowerCamelCase )]      ,					_lowerCamelCase )






@require_torch
class   __UpperCAmelCase		(      unittest.TestCase      ):

				@slow
				def 						_a      ( self ):
										lowerCamelCase__   =2  # number of steps of autoregressive prediction we will perform
										lowerCamelCase__   =10  # defined by the RL environment, may be normalized
										lowerCamelCase__   =DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert" )
										lowerCamelCase__   =model.to(_lowerCamelCase )
										lowerCamelCase__   =model.config
										torch.manual_seed(0 )
										lowerCamelCase__   =torch.randn(1      ,					1      ,					config.state_dim ).to(device=_lowerCamelCase      ,					dtype=torch.floataa )  # env.reset()

										lowerCamelCase__   =torch.tensor(
										    [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]      ,					device=_lowerCamelCase )

										lowerCamelCase__   =torch.tensor(_lowerCamelCase      ,					device=_lowerCamelCase      ,					dtype=torch.floataa ).reshape(1      ,					1      ,					1 )
										lowerCamelCase__   =state
										lowerCamelCase__   =torch.zeros(1      ,					0      ,					config.act_dim      ,					device=_lowerCamelCase      ,					dtype=torch.floataa )
										lowerCamelCase__   =torch.zeros(1      ,					0      ,					device=_lowerCamelCase      ,					dtype=torch.floataa )
										lowerCamelCase__   =torch.tensor(0      ,					device=_lowerCamelCase      ,					dtype=torch.long ).reshape(1      ,					1 )

										for step in range(_lowerCamelCase ):
																lowerCamelCase__   =torch.cat([actions, torch.zeros(1      ,					1      ,					config.act_dim      ,					device=_lowerCamelCase )]      ,					dim=1 )
																lowerCamelCase__   =torch.cat([rewards, torch.zeros(1      ,					1      ,					device=_lowerCamelCase )]      ,					dim=1 )

																lowerCamelCase__   =torch.ones(1      ,					states.shape[1] ).to(dtype=torch.long      ,					device=states.device )

																with torch.no_grad():
																						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =model(
																						    states=_lowerCamelCase      ,					actions=_lowerCamelCase      ,					rewards=_lowerCamelCase      ,					returns_to_go=_lowerCamelCase      ,					timesteps=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					return_dict=_lowerCamelCase      ,					)

																self.assertEqual(action_pred.shape      ,					actions.shape )
																self.assertTrue(torch.allclose(action_pred[0, -1]      ,					expected_outputs[step]      ,					atol=1E-4 ) )
																lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =(  # env.step(action)
																    torch.randn(1      ,					1      ,					config.state_dim ).to(device=_lowerCamelCase      ,					dtype=torch.floataa ),
																    1.0,
																    False,
																    {},
																)

																lowerCamelCase__   =action_pred[0, -1]
																lowerCamelCase__   =torch.cat([states, state]      ,					dim=1 )
																lowerCamelCase__   =returns_to_go[0, -1] - reward
																lowerCamelCase__   =torch.cat([returns_to_go, pred_return.reshape(1      ,					1      ,					1 )]      ,					dim=1 )
																lowerCamelCase__   =torch.cat(
																    [timesteps, torch.ones((1, 1)      ,					device=_lowerCamelCase      ,					dtype=torch.long ) * (step + 1)]      ,					dim=1 )



"""simple docstring"""


from __future__ import annotations

import math





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if len(__lowerCAmelCase							) != 2 or len(a[0]							) != 2 or len(__lowerCAmelCase							) != 2 or len(b[0]							) != 2:
												raise Exception("Matrices are not 2x2"							)
						lowerCamelCase__   =[
						    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
						    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
						]
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Dict:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[list, list, list, list]:




						'''simple docstring'''



						if len(__lowerCAmelCase							) % 2 != 0 or len(a[0]							) % 2 != 0:
												raise Exception("Odd matrices are not supported!"							)

						lowerCamelCase__   =len(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_length // 2

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[
						    [a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)
						]

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)]

						return top_left, top_right, bot_left, bot_right





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[int, int]:




						'''simple docstring'''



						return len(__lowerCAmelCase							), len(matrix[0]							)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   None:




						'''simple docstring'''



						print("\n".join(str(__lowerCAmelCase							) for line in matrix							)							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							) == (2, 2):
												return default_matrix_multiplication(__lowerCAmelCase  ,       __lowerCAmelCase							)

						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)
						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)

						lowerCamelCase__   =matrix_addition(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_subtraction(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)

						# construct the new matrix from our 4 quadrants
						lowerCamelCase__   =[]
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(top_left[i] + top_right[i]							)
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(bot_left[i] + bot_right[i]							)
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							)[1] != matrix_dimensions(__lowerCAmelCase							)[0]:
												lowerCamelCase__   =(
												    "Unable to multiply these matrices, please check the dimensions.\n"
												    F'''Matrix A: {matrixa}\n'''
												    F'''Matrix B: {matrixa}'''
												)
												raise Exception(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)

						if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
												return [matrixa, matrixa]

						lowerCamelCase__   =max(*__lowerCAmelCase  ,       *__lowerCAmelCase							)
						lowerCamelCase__   =int(math.pow(2  ,       math.ceil(math.loga(__lowerCAmelCase							)							)							)							)
						lowerCamelCase__   =matrixa
						lowerCamelCase__   =matrixa

						# Adding zeros to the matrices so that the arrays dimensions are the same and also
						# power of 2
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Removing the additional zeros
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								final_matrix[i].pop()
												else:
																		final_matrix.pop()
						return final_matrix


if __name__ == "__main__":
	a				=[
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 2, 3, 1],
	]
	a				=[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
	print(strassen(matrixa, matrixa))


